Start by clarifying requirements and scale, then design a pipeline that decouples ingestion from storage using a message queue. Propose a storage layer optimized for both real-time queries and long-term retention, and discuss trade-offs around consistency, latency, and cost.
Pro tip: Emphasize the importance of backpressure and data durability in async writes, and suggest a tiered storage approach (hot vs. cold) to balance real-time query performance with cost efficiency.
Ask about expected event volume, latency requirements for real-time queries, retention policies, and query patterns (e.g., ad-hoc debugging vs. dashboards).
Propose a scalable ingestion layer using a distributed message queue (e.g., Kafka) to handle async writes, with producers from services and consumers writing to storage.
Select a storage solution that supports fast writes and real-time queries, such as a time-series database (e.g., Prometheus for metrics) and a log search engine (e.g., Elasticsearch for logs), possibly with tiered storage.
Design a query layer that can serve real-time data from hot storage and historical data from cold storage, with caching and pre-aggregation for dashboards.
Discuss trade-offs: consistency vs. availability, latency vs. cost, and how to handle failures, backpressure, and data retention.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.